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Chainable functional API for list processing (map/filter/reduce). Great for data pipelines. Perfect for AI agents and LLM applications.

Project description

pystreams

Chainable functional API for list processing

Installation

pip install pystreams

💡 Usage Examples

Basic Operations

from pylib_streams import Stream, map, filter, reduce

# Using Stream class
result = Stream([1, 2, 3, 4, 5])\
    .map(lambda x: x * 2)\
    .filter(lambda x: x > 5)\
    .to_list()
# [6, 8, 10]

# Using functional API
doubled = map(lambda x: x * 2, [1, 2, 3])
filtered = filter(lambda x: x > 2, [1, 2, 3, 4])
summed = reduce(lambda a, b: a + b, [1, 2, 3, 4])
# 10

AI/ML Use Cases

from pylib_streams import Stream, map, filter, reduce

# Process ML predictions
predictions = Stream([0.1, 0.5, 0.9, 0.3])\
    .filter(lambda p: p > 0.5)\
    .map(lambda p: "high" if p > 0.8 else "medium")\
    .to_list()

# Transform training data
features = Stream(raw_data)\
    .map(extract_features)\
    .filter(is_valid)\
    .to_list()

📚 API Reference

See package documentation for complete API reference.

🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:

  • Clear function names and signatures
  • Comprehensive docstrings with examples
  • Type hints for better IDE support
  • Common use cases documented
  • Zero dependencies for reliability

License

MIT

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